Price: \$1,999.00

Length: 2 Days

Monte Carlo Simulation Training

The Monte Carlo simulation is used to estimate the probability of a certain income.

Consequently, Monte Carlo simulation is now widely used by investors and financial analysts to evaluate the probable success of investments they’re considering.

In fact, the Monte Carlo simulation has numerous applications in finance and other fields. For example, Monte Carlo is used in corporate finance to model components of project cash flow, which are impacted by uncertainty. The result is a range of net present values (NPVs) along with observations on the average NPV of the investment under analysis and its volatility.

Monte Carlo is also widely used for option pricing where numerous random paths for the price of an underlying asset are generated, each having an associated payoff. These payoffs are then discounted back to the present and averaged to get the option price. It is similarly used for pricing fixed income securities and interest rate derivatives.

However, Monte Carlo simulation is especially useful in portfolio management and personal financial planning.

But with the recent surge in Monte Carlo’s simulation popularity, its use has soared beyond the financial world to businesses in general and even the science realm.

Scientists have turned to Monte Carlo simulation in the area of high-energy physics. In the quantum (very small-scale) world, things are not easily observable and this is especially true at the point of collision in a particle accelerator.

Monte Carlo methods allow physicists to run simulations of these events, based on the Standard Model, and parameters which have been determined from previous experiments.

The basic principle of the Monte Carlo simulation lies in ergodicity, which describes the statistical behavior of a moving point in an enclosed system. The moving point will eventually pass through every possible location in an ergodic system. This becomes the basis of the Monte Carlo simulation, in which the computer runs enough simulations to produce the eventual outcome of different inputs.

Monte Carlo Simulation is sometimes compared to machine learning. However, machine learning (ML) is a computer technology that uses a large sample of input and output (I/O) data to train software to understand the correlation between both.

A Monte Carlo Simulation, on the other hand, uses samples of input data and a known mathematical model to predict probable outcomes occurring in a system. You use ML models to test and confirm the results in Monte Carlo simulations.

Monte Carlo Simulation Training by Tonex

Monte Carlo Simulation Training is a 2-day presenting two types of Monte Carlo simulations.

Monte Carlo simulation is a method for performing calculations when you have uncertainty about the inputs.  Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty in engineering projects, project management, cost, and other forecasting models.

Risk analysis is part of every decision we make, and we must face uncertainty, ambiguity, and variability. Monte Carlo simulation or Method allows us to see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty.

Monte Carlo Simulation Training course introduces fundamental issues in simulation-based analysis and Monte Carlo-based computing.  Participants will learn about rigorous analysis and interpretation and an objective treatment of various approaches.

Course Objectives

After the completion of this course, the students will be able to:

• Learn the fundamentals of modeling and simulation (M&S)
• Review and discuss principles and algorithms for simulation
• Describe the key concepts and terminology of Monte Carlo Simulation
• Review Monte Carlo Simulation Methods
• Learn about Limitations and Assumptions with methods in simulation and Monte Carlo
• Apply Monte Carlo Simulation to specific project
• Describe key real-world application of Monte Carlo Simulation Methods
• Describe principles and theory of Monte Carlo Simulation Methods for systems, systems of systems (SoS), capabilities and systems engineering
• Gain insight into complex systems, capabilities and difficult problems through use of Monte Carlo Simulation Methods
• Discuss Monte Carlo Simulation Methods and techniques for combat, Electronic Warfare, threats and combat, and cybersecurity
• Discuss Monte Carlo Simulation Methods and techniques for other domains such as aerospace, deep space, transportation, functional safety, manufacturing, power and energy, cyber security, health care, training/education, weather forecasting, infrastructure, and testing

Course Topics

Introduction to Monte Carlo Simulation

• What is Monte Carlo Simulation?
• How does Monte Carlo Simulation Work?
• Risk Analysis 101
• Statistics, Probability and Forecasting 101
• Monte Carlo Simulation Applied Domains
• Monte Carlo Simulation vs. Deterministic, or “single-point estimate” Analysis

Principles of Monte Carlo Simulation Method

• Monte Carlo Simulation as a Computerized Mathematical Technique
• Risk in Quantitative Analysis and Decision Making
• Possible Outcomes and the Probabilities
• Estimating Ranges of Values
• Basic Forecasting Model
• Forecasting Model Using Range Estimates
• Model building
• Model Selection
• Fisher information Matrix
• Model Fitting
• Random Number Generation
• Stochastic Simulations
• Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm
• Simulation-based Optimization by Gradient-free Methods
• Finite difference stochastic approximation (FDSA)
• Markov chain Monte Carlo (MCMC)

Forecasting with Monte Carlo Simulation

• Probability of Completion Within Specified Time
• Extreme Possibilities
• Principles of Probability Distribution
• Using probability distributions, variables
• Common probability distributions
• Normal Or “bell curve”
• Lognormal
• Uniform
• Triangular
• PERT
• Discrete

Monte Carlo Simulation Accuracy

• Probabilistic Results
• Sensitivity Analysis
• Distribution Models
• Uniform Distribution
• Discrete Distribution
• Normal Distribution
• Triangular Distribution
• Beta-PERT Distribution

Case Studies